Learning to Export from Neighbors

This paper studies how learning from neighboring firms affects new exporters’ performance and dynamics. We develop a statistical decision model in which a firm updates its prior belief about demand of a foreign market based on the number of neighbors currently selling there, the level and heterogeneity of their export sales, and the firm’s own prior knowledge about the market. A positive signal about demand inferred from neighbors’ export performance raises the firm’s probability of entry and initial sales in the market, but lowers post-entry growth, conditional on survival. These learning effects are stronger when there are more neighbors revealing the signal or when the firm is less familiar with the market. Decisions to exit are independent of the prevalence of neighboring export activities. We find supporting evidence from the transaction-level export data for all Chinese exporters over 2000-2006. Our findings are robust to controlling for firms’ supply shocks, countries’ demand shocks, and city-country fixed effects.